This chapter presents the use of the DSK6713 to demonstrate the features of Amplitude Modulation (AM) transmission and reception. The model runs in real-time and enables the use to select the AM detector as well as the transmission and reception parameters (modulation index and carrier frequency).

Discovering quirks in MATLAB, finding a simple noise filter, and getting great sound!

Approximating continuous time on a computer. Creating signals, doing things to them like convolution, and plotting them.

The Audio Conference Bridge enables a voice call with multiple (n >2) attendants. The algorithm monitors the voice signals from all attendants, and creates the signals to be transmitted to the attendants. In this module the implementation of such a bridge is described, The implementation is based on the integration of user-specific driver with the Simulink environment building blocks.

This course has been created as an introduction to audio localization, and how beamforming can be applied in a real-time environment.

This module covers basic complex and matrix operations in an m-file environment.

This module covers basic mathematical operations in an m-file environment.

This course covers sensing and measurement for quantitative molecular/cell/tissue analysis, in terms of genetic, biochemical, and biophysical properties. Methods include light and fluorescence microscopies; electro-mechanical probes such as atomic force microscopy, laser and magnetic traps, and MEMS devices; and the application of statistics, probability and noise analysis to experimental data.

This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done on the MIT Server in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.

Description of a MATLAB GUI that identifies bird calls within audio wave files.

The project group began by obtaining MATLAB data on these five types of ECG signals. The signals obtained might be thought of as the ideal; that is they were completely free of noise, perfectly smooth, textbook examples of what each type of signal might look like. A random noise signal was then generated by use of a random number generator. The level of noise could be varied by adjusting the amplitude of this noise signal. The noise signal was then added to the ideal signals to create a simulation of the type of measurement that might be found in the real world. The group then applied filters and identification algorithms to see if the original signal could be correctly identified. Our identification method worked as follows. To reduce noise we convolved the unknown signal with a sinc function of width seven. Seven was chosen because smaller values did not have adequate noise reduction and larger values had the adverse effect of essentially "smoothing out" the signal to too great a degree. We then took an inner product of the filtered signal with all of the idealized signals. Whichever inner product was the greatest we took to correspond with the correct signal. That is to say that if the inner product of the unknown signal and the sinus rhythm was greater than the inner product of the unknown signal with any other of our ideal signals we take the unknown rhythm to be a sinus rhythm. For flatline we simply stated that if the maximum value of the signal does not exceed some threshold our cardiac rhythm is flatline.

Are you interested in building and testing your own imaging radar system? MIT Lincoln Laboratory offers this 3-week course in the design, fabrication, and test of a laptop-based radar sensor capable of measuring Doppler, range, and forming synthetic aperture radar (SAR) images. You do not have to be a radar engineer but it helps if you are interested in any of the following; electronics, amateur radio, physics, or electromagnetics. It is recommended that you have some familiarity with MATLAB®. Teams of three students will receive a radar kit and will attend a total of 5 sessions spanning topics from the fundamentals of radar to SAR imaging. Experiments will be performed each week as the radar kit is implemented. You will bring your radar kit into the field and perform additional experiments such as measuring the speed of passing cars or plotting the range of moving targets. A final SAR imaging contest will test your ability to form a SAR image of a target scene of your choice from around campus; the most detailed and most creative image wins.

This module explains how the car simulation is implemented in Matlab.

This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, MATLAB®, and Mathematica®. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages. Students first learn the basic usage of each language, common types of problems encountered, and techniques for solving a variety of problems encountered in contemporary research: examination of data with visualization techniques, numerical analysis, and methods of dissemination and verification. No prior programming experience is required.

This course introduces programming languages and techniques used by physical scientists: FORTRAN, C, C++, Matlab, and Mathematica. Emphasis is placed on program design, algorithm development and verification, and comparative advantages and disadvantages of different languages.

Covers computational and data analysis techniques for environmental engineering applications. First third of subject introduces MATLAB and numerical modeling. Second third emphasizes probabilistic concepts used in data analysis. Final third provides experience with statistical methods for analyzing field and laboratory data. Numerical techniques such as Monte Carlo simulation are used to illustrate the effects of variability and sampling. Concepts are illustrated with environmental examples and data sets. This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

In our conclusion, we compare our original signal with our favorite distortion signal.

Introduction to continuous time signal analysis. Basic signals including impulses, pulses, and unit steps. Periodic signals. Convolution of signals. Fourier series and transforms in discrete and continuous time. Computer laboratory.

In this lab, we will explore convolution and how it can be used with signals such as audio.